{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "56957b7f-e289-4999-8a40-ce1a8378d8cd",
   "metadata": {},
   "source": [
    "# Unit Test Generator\n",
    "\n",
    "The requirement: use a Frontier model to generate fast and repeatable unit tests for Python code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ef67ef0-1bda-45bb-abca-f003217602d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "import io\n",
    "import sys\n",
    "import ast\n",
    "import unittest, contextlib\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "import google.generativeai\n",
    "import anthropic\n",
    "from IPython.display import Markdown, display, update_display\n",
    "import gradio as gr\n",
    "import subprocess\n",
    "\n",
    "# environment\n",
    "\n",
    "load_dotenv(override=True)\n",
    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
    "os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
    "\n",
    "openai = OpenAI()\n",
    "claude = anthropic.Anthropic()\n",
    "OPENAI_MODEL = \"gpt-4o\"\n",
    "CLAUDE_MODEL = \"claude-3-7-sonnet-20250219\"\n",
    "\n",
    "system_message = \"You are an assistant that implements unit testing for Python code. \"\n",
    "system_message += \"Respond only with Python code; use comments sparingly and do not provide any explanation other than occasional comments. \"\n",
    "\n",
    "def remove_main_block_from_code(code):\n",
    "    \"\"\"\n",
    "    Remove top-level `if __name__ == \"__main__\":` blocks from code.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        tree = ast.parse(code)\n",
    "\n",
    "        class RemoveMain(ast.NodeTransformer):\n",
    "            def visit_If(self, node):\n",
    "                # check if this is: if __name__ == \"__main__\":\n",
    "                test = node.test\n",
    "                if (\n",
    "                    isinstance(test, ast.Compare) and\n",
    "                    isinstance(test.left, ast.Name) and\n",
    "                    test.left.id == \"__name__\" and\n",
    "                    len(test.ops) == 1 and isinstance(test.ops[0], ast.Eq) and\n",
    "                    len(test.comparators) == 1 and\n",
    "                    isinstance(test.comparators[0], ast.Constant) and\n",
    "                    test.comparators[0].value == \"__main__\"\n",
    "                ):\n",
    "                    return None  # remove this node entirely\n",
    "                return node\n",
    "\n",
    "        tree = RemoveMain().visit(tree)\n",
    "        ast.fix_missing_locations(tree)\n",
    "        return ast.unparse(tree)  # get back code as string\n",
    "    except Exception as e:\n",
    "        print(\"Error removing __main__ block:\", e)\n",
    "        return code  # fallback: return original code if AST fails\n",
    "\n",
    "def user_prompt_for(python_file):\n",
    "    if isinstance(python_file, dict):  # from Gradio\n",
    "        file_path = python_file[\"name\"]\n",
    "    elif hasattr(python_file, \"name\"):  # tempfile\n",
    "        file_path = python_file.name\n",
    "    else:  # string path\n",
    "        file_path = python_file\n",
    "\n",
    "    with open(file_path, \"r\", encoding=\"utf-8\") as f:\n",
    "        python_code = f.read()\n",
    "\n",
    "    # strip __main__ blocks\n",
    "    python_code = remove_main_block_from_code(python_code)\n",
    "\n",
    "    user_prompt = \"Write unit tests for this Python code. \"\n",
    "    user_prompt += \"Respond only with Python code; do not explain your work other than a few comments. \"\n",
    "    user_prompt += \"The unit testing is done in Jupyterlab, so you should use packages that play nicely with the Jupyter kernel. \\n\\n\"\n",
    "    user_prompt += \"Include the original Python code in your generated output so that I can run all in one fell swoop.\\n\\n\"\n",
    "    user_prompt += python_code\n",
    "\n",
    "    return user_prompt\n",
    "\n",
    "def messages_for(python_file):\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_message},\n",
    "        {\"role\": \"user\", \"content\": user_prompt_for(python_file)}\n",
    "    ]\n",
    "\t\n",
    "def stream_gpt(python_file):    \n",
    "    stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python_file), stream=True)\n",
    "    reply = \"\"\n",
    "    for chunk in stream:\n",
    "        fragment = chunk.choices[0].delta.content or \"\"\n",
    "        reply += fragment\n",
    "        yield reply.replace('```python\\n','').replace('```','')\n",
    "\t\t\n",
    "def stream_claude(python_file):\n",
    "    result = claude.messages.stream(\n",
    "        model=CLAUDE_MODEL,\n",
    "        max_tokens=2000,\n",
    "        system=system_message,\n",
    "        messages=[{\"role\": \"user\", \"content\": user_prompt_for(python_file)}],\n",
    "    )\n",
    "    reply = \"\"\n",
    "    with result as stream:\n",
    "        for text in stream.text_stream:\n",
    "            reply += text\n",
    "            yield reply.replace('```python\\n','').replace('```','')\n",
    "\t\t\t\n",
    "def unit_test(python_file, model):\n",
    "    if model==\"GPT\":\n",
    "        result = stream_gpt(python_file)\n",
    "    elif model==\"Claude\":\n",
    "        result = stream_claude(python_file)\n",
    "    else:\n",
    "        raise ValueError(\"Unknown model\")\n",
    "    for stream_so_far in result:\n",
    "        yield stream_so_far\n",
    "\n",
    "def execute_python(code):\n",
    "    buffer = io.StringIO()\n",
    "    try:\n",
    "        with contextlib.redirect_stdout(buffer), contextlib.redirect_stderr(buffer):\n",
    "            # execute code in isolated namespace\n",
    "            ns = {}\n",
    "            exec(code, ns)\n",
    "\n",
    "            # manually collect TestCase subclasses\n",
    "            test_cases = [\n",
    "                obj for obj in ns.values()\n",
    "                if isinstance(obj, type) and issubclass(obj, unittest.TestCase)\n",
    "            ]\n",
    "            if test_cases:\n",
    "                suite = unittest.TestSuite()\n",
    "                for case in test_cases:\n",
    "                    suite.addTests(unittest.defaultTestLoader.loadTestsFromTestCase(case))\n",
    "                runner = unittest.TextTestRunner(stream=buffer, verbosity=2)\n",
    "                runner.run(suite)\n",
    "    except Exception as e:\n",
    "        print(f\"Error during execution: {e}\", file=buffer)\n",
    "\n",
    "    return buffer.getvalue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "670b8b78-0b13-488a-9533-59802b2fe101",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Gradio UI ---\n",
    "with gr.Blocks() as ui:\n",
    "    gr.Markdown(\"## Unit Test Generator\\nUpload a Python file and get structured unit testing.\")\n",
    "    with gr.Row(): # Row 1\n",
    "        orig_code = gr.File(label=\"Upload your Python file\", file_types=[\".py\"])\n",
    "        test_code = gr.Textbox(label=\"Unit test code:\", lines=10)\n",
    "    with gr.Row(): # Row 2\n",
    "        model = gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")\n",
    "    with gr.Row(): # Row 3\n",
    "        generate = gr.Button(\"Generate unit test code\")\n",
    "    with gr.Row(): # Row 4\n",
    "        unit_run = gr.Button(\"Run Python unit test\")\n",
    "    with gr.Row(): # Row 5\n",
    "        test_out = gr.Textbox(label=\"Unit test result:\", lines=10)\n",
    "\n",
    "    generate.click(unit_test, inputs=[orig_code, model], outputs=[test_code])\n",
    "\n",
    "    unit_run.click(execute_python, inputs=[test_code], outputs=[test_out])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "609bbdc3-1e1c-4538-91dd-7d13134ab381",
   "metadata": {},
   "outputs": [],
   "source": [
    "ui.launch(inbrowser=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
